Type of position: PhD
Name: Fernando Ugalde Green
Description: Looking for a PhD position in Machine Learning, Deep Learning, and/or Uncertainty Quantification, beginning in 2027. I'm currently finishing my MSc on Computer Science at ITCR in collaboration with the LHCb experiment at CERN. My research focuses on Uncertainty Quantification algorithms (Monte Carlo Dropout, BNNs, Feature Densities) for anomaly detection in Generative Adversarial Networks for high-energy physics simulations, with immediate application to other domains.
Short CV:
BSc in Computer Engineering, Costa Rica Institute of Technology (2019-2024)
MSc in Computer Science, Costa Rica Institute of Technology (2025-2026)
Published research:
Aleatoric and epistemic uncertainty proxies for GAN-based simulations of the LHCb experiment (7th IEEE International Conference on BioInspired Processing, 2025)
Uncertainty Quantification for a Cramér Generative Adversarial Network-Based Simulation of the Ring-Imaging Cherenkov Particle Detector Using Feature Densities (Springer EPJ Research Infrastructures, 2026)
Email: fernando.ugalde.green@cern.ch
ORCID: 0009-0002-6527-2333
Web page:
Other link: https://scholar.google.com/citations?user=7vZx9-kAAAAJ
General location: Asia, Europe, North America, South America
Specific countries:
Until when: 2026-10-01
Present at next GECCO? yes